Algorithms for early detection of silent liver fibrosis in the primary care setting - a scoping review

Fatty liver disease affects almost 30% of the adult population worldwide. Most patients are asymptomatic, and there is not a linear relationship between exposure to risk factors and the risk of developing fibrosis. The combination of a very large, asymptomatic risk population where only a few percent will develop life-threatening liver disease is a growing diagnostic challenge for the health services. Accurate fibrosis assessment in primary care is limited by poor correlation with liver blood tests and low availability of elastography. Non-invasive tests are promising tools, but little is known about their diagnostic accuracy in low-risk populations.

A scoping review was conducted to identify articles that focused on the current use of biomarkers and algorithms in primary care for the detection of patients with fatty liver disease in need of referral for further work-up.

Currently available algorithms for targeted screening for liver fibrosis perform better than the individual routine liver blood tests or liver ultrasonography. However, primary care physicians urgently need algorithms with even higher diagnostic accuracies than what is available today. The main limitation of the existing widely accessible algorithms, such as the FIB-4, is the large number of false-positive tests, resulting in overdiagnosis and futile referrals to secondary care.